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Application of ASTER multispectral data and hyperspectral spectroscopy for phosphate exploration
Published in Christoph Mueller, Winfred Assibey-Bonsu, Ernest Baafi, Christoph Dauber, Chris Doran, Marek Jerzy Jaszczuk, Oleg Nagovitsyn, Mining Goes Digital, 2019
N. Mezned, A. Fatnassi, S. Abdeljaouad
Moreover, multispectral data was pre-processed before phosphate mapping. Indeed, the ASTER (Advanced Spaceborne Thermal Emission and Reflection) data, which was acquired on September 28, 2007, was used in this study. ASTER sensor provides seasonal coverage of the overall weight to a spatial resolution of 15 m for the VNIR, of 30 m for the SWIR (Short Waves InfraRed), and 90 m for the TIR (Thermal InfraRed). All images were georeferenced to UTM zone 32 North projection with WGS84 datum. An image subset was derived after that for VNIR and SWIR images and was radiometrically normalized using the Internal Average Relative Reflection (IARR) to estimate surface spectral reflectance. This methodology was previously well presented and introduced as an efficient method for similar areas (Hosseinjani et al., 2005; Hosseinjani et al., 2011; Guha et al., 2018). The TIR image was calibrated using in-scene-atmospheric correction (ISAC) method (Guha et al., 2015) and an emissivity normalization method was after that implemented for deriving ASTER based emissivity (Guha, 2014).
Image Statistics
Published in Morton John Canty, Image Analysis, Classification, and Change Detection in Remote Sensing, 2019
Listing 2.1 shows the Python class Cpm, which is part of the auxil package. The update() method takes as input a single row of a multi-spectral image in the form of a data matrix Xs, together with weights Ws if desired. The provmeans function called in line 20 is coded in C and accessed with Python’s ctypes package. It loops through the pixels in the Xs array, updating the mean self.mn and upper diagonal part of the covariance array self.cov as it goes. Thus we can calculate the covariance matrix of the VNIR bands of the ASTER image as follows: from osgeo import gdal from osgeo . gdalconst import GA_ReadOnly import auxil.auxil1 as auxil gdal.AllRegister () infile = ’imagery/ AST_20070501’ inDataset = gdal.Open(infile, GA_ReadOnly) cols = inDataset.RasterXSize rows = inDataset.RasterYSize Xs = np.zeros ((cols ,3)) cpm = auxil.Cpm(3) rasterBands = [inDataset.GetRasterBand(k+1) for k in range(3)] for row in range( rows ): for k in range(3): Xs[:,k]=rasterBands[k].ReadAsArray(0, row, cols, 1) cpm.update(Xs) print cpm.covariance()
Flood Mapping, Monitoring, and Damage Assessment
Published in Saeid Eslamian, Faezeh Eslamian, Flood Handbook, 2022
Vaibhav Garg, Shivani Pathak, Jyoti Rathour, Saeid Eslamian
The moderate spatial resolution (Landsat, SPOT, ASTER, and Sentinel-2) remote sensing data have been extensively used in flood mapping. It was reported that using such data, users have achieved as high as 5% error in flood inundation mapping (Rango and Salomonson, 1974; Rahman and Li, 2016). The limited coverage and the cost associated with the very high spatial resolution optical data make them less attractive for flood extent mapping (Rahman and Li, 2016). The high temporal resolution of coarser resolution (NOAA, AVHRR, MODIS) optical data makes them attractive for large-scale flood mapping. The Dartmouth Flood Observatory (http://floodobservatory.colorado.edu/index.html) mostly uses the MODIS data for mapping and monitoring floods all over the globe. NASA's MODIS sensor, onboard Terra and Aqua, provides 250 m spatial resolution image twice daily. The Dartmouth Flood Observatory NASA-MODIS Rapid Response Record of 2011 Bihar Flooding is shown in Figure 17.12. The band 1 and 2 data of MODIS were processed to map water/flood extent. The red1 shows the extent of flooding during the day of acquisition (August 2011), where dark blue* is the surface water prior to the flooding (March 2000). The light red* shows flood occurred in 2011 before August 2011, but where land is now dry. On the other hand, the light blue* shows frequent flooding regions mapped by MODIS data from early 2000 to 2011. The follow-on sensor VIIRS is extending such large-scale flood mapping/sensing capabilities.
Identifying hydrothermally altered rocks using ASTER satellite imageries in Eastern Anti-Atlas of Morocco: a case study from Imiter silver mine
Published in International Journal of Image and Data Fusion, 2022
Youssef Atif, Abderrahmane Soulaimani, Atman Ait lamqadem, Amin Beiranvand Pour, Biswajeet Pradhan, El Aouad Nouamane, Kharis Abdelali, Aidy M Muslim, Mohammad Shawkat Hossain
Hydrothermal deposits are typically associated with a significant transformation of rocks adjacent to ore mineralisation zones (Pour and Hashim 2012, Mathieu 2018). The presence of hydrothermal alteration zones in Imiter silver mining are of Eastern Anti-Atlas of Morocco is documented (Baroudi et al. 1999) and confirmed by remote-sensing analysis in this investigation. Alteration is the result of the transformation of the rock components by hydrothermal fluid circulation. Therefore, alteration zones are important indicators of ore mineralisation in the Imiter region. When the rocks are injected with ore fluids, the composition is modified to alteration mineralogical assemblages by hydrothermal fluid that can be mapped using multispectral remote-sensing satellite sensor such ASTER data due to their spectral properties in the VNIR and SWIR regions.
Integration of SPOT-5 and ASTER satellite data for structural tracing and hydrothermal alteration mineral mapping: implications for Cu–Au prospecting
Published in International Journal of Image and Data Fusion, 2018
Reyhaneh Ahmadirouhani, Mohammad-Hassan Karimpour, Behnam Rahimi, Azadeh Malekzadeh-Shafaroudi, Amin Beiranvand Pour, Biswajeet Pradhan
This investigation is concerned with integration of SPOT-5 and ASTER remote sensing satellite data for information extraction to trace geological structural elements and highlight hydrothermal alteration zones for prospecting Cu–Fe–Au vein-type mineralisation in the Bajestan region. The objectives of this research are: (i) to detect and discriminate propylitic, phyllic, argillic and gossan alteration zones using visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER; and (ii) to identify the high potential zones of Cu–Fe–Au vein-type mineralisation in the Bajestan region concerning specific faults and fractures trends and concentration, as well as the type of hydrothermal alteration zones.
Fusion of ASTER satellite imagery, geochemical and geology data for gold prospecting in the Astaneh granite intrusive, West Central Iran
Published in International Journal of Image and Data Fusion, 2022
Hooman Moradpour, Ghodratollah Rostami Paydar, Bakhtiar Feizizadeh, Thomas Blaschke, Amin Beiranvand Pour, Khalil Valizadeh Kamran, Aidy M Muslim, Mohammad Shawkat Hossain
Remote sensing spectral data for mapping hydrothermal alteration minerals and zones are extracted from ASTER VNIR+SWIR bands. Subsequently, the alteration mineral layers are fused into geochemical, geological and structural layers using Fuzzy Logic Model (FLM) to generate gold potential map for the Astaneh granite intrusive and surrounding area. Figure 2 shows the flowchart of the methodology applied in this study. For processing ASTER data, lineament extraction and fusing the datasets used in this study, the ENVI (Environment for Visualising Images) version 5.3, PCI Geomatica 2018 and ArcGIS version 10.5 software packages were exploited, respectively.